INTEREST CATEGORY: INNOVATION AND TECH
POSTING TYPE: Revisits

Author: Linda Hollebeek

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Journal of Product Innovation Management

Special Issue Call for Papers

“Artificial Intelligence, Stakeholder Engagement, and Innovation Value”

Manuscript submission deadline: October 30, 2023

Guest Editors:

• Praveen Kopallé, Ph.D., Dartmouth College.
• Costas Katsikeas, Ph.D., University of Leeds. Giampaolo Viglia, Ph.D., University of Portsmouth and Università della Valle d’Aosta.
• Linda D. Hollebeek, Ph.D., Vilnius University, Tallinn University of Technology, Umea University, Lund University, and University of Johannesburg.

Special Issue Motivation and Focus

Managers increasingly adopt, incorporate, and rely on an ever-growing range of artificial intelligence (AI)-based technologies and applications (Kopallé et al., 2022; Wetzels, 2021; Garbuio & Nidthida, 2021). For example, companies such as Amazon or IBM are using artificially intelligent innovations in their human resource management (HRM) planning and implementation (e.g., by scanning candidates’ résumés, recording and interpreting interviewees’ responses, and managing employee churn; Bailey et al., 2019; Vrontis et al., 2021). Likewise, firms like Tesco and Starbucks are leveraging marketing-based AI applications, by providing product recommendations to (prospective) customers, personalizing brand-related content, segmenting customers, and optimizing their pricing strategies, among others (Verganti et al. 2020; Huang & Rust, 2021). Moreover, operations management-based AI applications, which can be used to facilitate tasks including production/sales forecasting, operation-related anomaly prediction, and (big) data analytics, have also taken off in leading organizations including Walmart, Philips, and eBay, to name a few (Cappa et al., 2021; Raisch & Krakowski, 2021).

Key benefits of these AI-based innovations include enhanced efficiency, improved prediction (e.g., of individuals’ needs or behaviors), and reduced human error and time to market (Hollebeek et al., 2021), thus offering significant value to firms and their stakeholders (Eisingerich et al., 2021; Brynjolfsson et al., 2019). Quantifying these benefits, global consultancy firm Gartner has forecast AI technology to achieve a compound annual growth rate of 28%, from $692bn in 2017 to $5.025 trillion in 2025 (Lovelock et al., 2018). Likewise, PwC (2021) estimates that “AI could contribute up to $15.7 trillion to the global economy in 2030.” Particularly in a (post-)Covid environment featuring a growing desire for contactless interactions, the demand for AI-based solutions is surging (Khemasuwan & Colt, 2021; Spanjol & Noble, 2020).

Given AI’s potentially transformative impact on innovation management, literature in this area is rapidly emerging (Garbuio et al., 2021; Haefner et al., 2021). However, little remains known regarding the drivers, dynamics, characteristics, and outcomes of AI adoption on different organizational stakeholders’ engagement with these applications (e.g., Kumar et al., 2019; Hughes et al., 2019) and their perceived AI-based innovation value (Ganotakis & Love, 2012), thus exposing a pertinent literature-based gap and leaving managers in the dark regarding these issues that are of growing importance to their business (Guha et al., 2021; Hyve, 2019).

With its stakeholder theory-informed roots that date back to the 1980s (e.g., Freeman, 1984), stakeholder engagement is subject to a rich academic discourse (e.g., Donaldson and Preston, 1995; Watson et al., 2018), in which stakeholders are viewed as “any group or individual who can affect or is affected by [the firm]” (Freeman 1984, p. 46), including its employees, customers, suppliers, investors, owners, strategic partners, competitors, government, the media, local community organizations, etc. In this literature stream, stakeholder engagement, defined as “a stakeholder’s [cognitive, emotional, and behavioral] resource endowment in his/her role-related interactions, activities, and/or relationships” (Hollebeek et al., 2022, p. 328), has been identified as a key stakeholder management metric (Viglia et al., 2018; Menguc et al., 2017; O’Riordan & Fairbrass, 2014). In particular, elevated stakeholder engagement is conducive to enhanced stakeholder trust, dialogue, and collaboration, in turn boosting (e.g., innovation-related) returns and stakeholder-perceived value (e.g., Katsikeas et al., 2016; Moreau, 2011). However, as specific firm stakeholders’ needs, interests, and engagement typically differ and may even oppose one another (Freeman et al., 2010), little is known regarding different firm stakeholders’ engagement with AI-based applications or their drivers, dynamics, potential tensions, and consequences, whether for the focal stakeholder, other stakeholders, or the firm (e.g., Wijayati et al., 2022; Perez-Vega et al., 2021), thus warranting further exploration.

In other words, despite the advances made in the individual topic areas of AI, stakeholder engagement, and innovation value, understanding of the intersection of different stakeholders’ engagement with specific AI-based applications and their perceived innovation value lags behind. For example, while authors, including Heller et al. (2021) or Eisingerich et al. (2021) explore AI’s role in fostering customer engagement, understanding of its effect on other or multiple stakeholders’ engagement – while pivotal for firms – remains limited (e.g., Huang et al., 2021; Sjödin et al. 2019; PwC, 2019), necessitating further exploration.

In response to this gap, this interdisciplinary Special Issue solicits state-of-the-art submissions that explore the role of AI applications in developing, nurturing, or optimizing specific stakeholders’ engagement (e.g., with the brand or firm) and perceived innovation value. To be considered for publication in the Special Issue, manuscripts should offer a substantial original contribution to AI-, stakeholder engagement-, and innovation management research. We welcome conceptual, methodological, qualitative, quantitative, or mixed-methods contributions grounded in a range of perspectives that offer insight into issues including, but not limited to, the following:

Conceptual development of the AI/stakeholder engagement interface

• What dynamics characterize different stakeholders’ engagement with particular AI-based innovations?
• Which theoretical lenses are appropriate for examining the AI, stakeholder engagement, and innovation management interface (e.g., stakeholder theory, technology acceptance model, diffusion of innovations, task/technology fit, unified theory of acceptance and use of technology, theory of inventive problem-solving, technology-organization-environment perspective, socio-technical perspective, or social exchange theory, to name a few)?

AI-based stakeholder engagement’s nomological network

• How do managers best nurture stakeholders’ AI-based technology readiness and buy-in, with a view to optimizing their AI-based engagement?
• How do particular AI applications drive the development of different stakeholders’ engagement, and what are its respective benefits and/or drawbacks for relevant stakeholders and the firm?

AI-based engagement’s impact on stakeholder-, innovation-, and firm performance

• What are critical success factors for AI-implementing firms in managing their stakeholders’ potentially conflicting needs, objectives, engagement, and perceived innovation value?
• How can AI be leveraged to boost stakeholders’ collaborative innovation value?
• How can firm-based capabilities be leveraged for optimal AI-based stakeholder/firm returns?
• How does the development of AI-based stakeholder engagement impact innovation- and firm performance?
• To what extent may firm-based divestiture from specific AI technology (e.g., SoftBank’s alleged discontinuation of its humanoid robot, Pepper; Metz, 2021) see an instant (vs. lagged) effect on AI-based stakeholder engagement, and what are its implications for innovation- or firm performance?
• During or post-Covid-19, how can AI-based stakeholder engagement be leveraged to optimize stakeholder-, innovation-, and firm performance?

Stakeholders’ potential AI-based differences, tensions, or vulnerabilities

• How can AI technologies be designed or applied to reconcile different stakeholders’ (e.g., clashing) needs, engagement, and perceived innovation value?
• How should the optimization of stakeholders’ AI-based engagement be designed into the firm’s (e.g., digital) business model (innovation)?
• How do firms ensure the compatibility, fit, and integration of stakeholders’ AI-based engagement (e.g., across stakeholders, firms, and/or technologies)?
• How may digitally disadvantaged or vulnerable stakeholders be impacted by the rise of firm-based AI applications, and what is its impact on their engagement with the firm and their perceived innovation value?

Methodological issues at the AI/stakeholder engagement interface

• Which research methodologies are particularly suited to the exploration of AI-based stakeholder engagement?
• What data is needed to explore the AI/stakeholder engagement interface?
• Which AI-based methods and approaches optimize AI-based stakeholder engagement and perceived innovation value?
• How can AI-based algorithms be used to leverage different stakeholders’ engagement?
• Are any methodological (e.g., big data-based) refinements necessary to move AI-based stakeholder engagement research forward?
• What scope is there to conduct meta-analyses addressing the AI/stakeholder engagement interface?

Deadline, Submission, and Review Process

The deadline for submissions to this Special Issue is October 30, 2023, via the Journal of Product Innovation Management’s online submission portal: https://mc.manuscriptcentral.com/jpim. When submitting your manuscript, please select the Special Issue (SI): AI-based Stakeholder Engagement from the drop-down menu. Submissions should be prepared in accordance with the Journal of Product Innovation Management’s editorial policy and author guidelines, which are available at: https://onlinelibrary.wiley.com/page/journal/15405885/homepage/forauthors.html.

The guest editors will screen submissions to ensure their suitable scope, fit with the journal’s aims and objectives, and relevance to the Special Issue topic. Manuscripts that do not pass the initial screening will be returned to the authors, while the others will be peer-reviewed in accordance with the Journal of Product Innovation Management’s guidelines and procedures. The Special Issue’s publication is planned for the second half of 2025.

Queries can be directed at the Special Issue Guest Editors:

Praveen Kopallé, Ph.D.
Professor of Marketing
Dartmouth College Email: praveen.k.kopalle@tuck.dartmouth.edu

Costas Katsikeas, Ph.D. Arnold Ziff Research Chair in Marketing & International Management
University of Leeds
Email: csk@lubs.leeds.ac.uk

Giampaolo Viglia, Ph.D.
Professor of Marketing University of Portsmouth and Università della Valle d’Aosta
Email: giampaolo.viglia@port.ac.uk

Linda D. Hollebeek, Ph.D.
Professor of Marketing
Vilnius University and Tallinn University of Technology
Email: linda.hollebeek@evaf.vu.lt

References

Bailey, D., Faraj, S., Hinds, P., von Kogh, G., & Leonardi, P. (2019). Special Issue of Organization Science: Emerging technologies and organizing. Organization Science, 30(3), 642-646.

Brynjolfsson, E., Hui, X., & Liu, M. (2019). Does machine translation affect international trade? Evidence from a large digital platform. Management Science, 65(12), 5449-5956.

Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021). Big data for creating and capturing value in the digitalized environment: Unpacking the effects of volume, variety, and veracity on firm performance. Journal of Product Innovation Management, 38(1), 49-67.

Donaldson, T. & Preston, L. (1995). The stakeholder theory of the corporation: Concepts, evidence, and implications. Academy of Management Review, 20(1), 65-91.

Eisingerich, A., Lin, Y., & Doong, H. (2021). Avatar design of virtual salespeople: Mitigation of recommendation conflicts. Journal of Service Research, 24(1), 141-159.

Freeman, R.E. (1984). Strategic Management: A Stakeholder Approach. Boston: Pitman.

Freeman, R., Harrison, J., Wicks, A., Parmar, B., & De Colle, S. (2010). Stakeholder theory: The state of the art. Cambridge: Cambridge University Press.

Ganotakis, P. & Love, J. (2012). The innovation value chain in new technology-based firms: Evidence from the U.K. Journal of Product Innovation Management, 29(5), 839-860.

Garbuio, M. & Nidthida, L. (2021). Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence. Journal of Product Innovation Management, 38(6), 701-725.

Guha, A., Grewal, D., Kopallé, P., Haenlein, M., Schneider, M., Jung, H., Moustafa, R., Hedge, D., & Hawkins, G. (2021). How artificial intelligence will affect the future of retailing. Journal of Retailing, 97(1), 28-41.

Heller, J., Chylinski, M., de Ruyter, K., Keeling, D., Hilken, T., & Mahr, D. (2021). Tangible service automation: Decomposing the technology-enabled engagement process (TEEP) for augmented reality. Journal of Service Research, 24(1), 84-103.

Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. Hollebeek, L., Kumar, V., & Srivastava, R.K. (2022). From customer-, to actor-, to stakeholder engagement: Taking stock, conceptualization, and future directions. Journal of Service Research, 25(2), 328-343.

Hollebeek, L., Sprott, D., & Brady, M. (2021). Rise of the machines? Customer engagement in automated service interactions. Journal of Service Research, 24(1), 3-8.

Huang, M.H., Malthouse, E., Noble, S., & Wetzels, M. (2021). Moving service research forward. Journal of Service Research, OnlineFirst, DOI: https://doi.org/10.1177/10946705211040022.

Huang, M.H. & Rust, R. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Artificial intelligence, employee engagement, fairness, and job outcomes. Managing Technology and Middle- and Low-skilled Employees (The Changing Context of Managing People), Emerald, 61-68.

Hyve (2019). Autonomous Innovation – How AI and algorithms revolutionize innovation management: Scientific study on the impact of artificial intelligence on innovation management. Accessed (Apr 7, 2022) at: www.hyve.net.

Katsikeas, C., Morgan, N., Leonidou, L., & Hult, G.T. (2016). Assessing performance outcomes in marketing. Journal of Marketing, 80(2), 1-20.

Khemasuwan, D. & Colt, H. (2021). Applications and challenges of AI-based algorithms in the COVID-19 pandemic. BMJ Innovations. Accessed (Jan 20, 2022) at: https://innovations.bmj.com/content/7/2/387.abstract.

Kopallé, P., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2022). Examining artificial intelligence in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522-540.

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155.

Lovelock, J., Tan, S., Hare, J., Woodward, A., & Priestley, A. (2018). Forecast: The business value of artificial intelligence, worldwide. Gartner (March 12; ID: G00348137), Accessed (Feb 18, 2022) at: https://www.gartner.com/en/documents/3868267/forecast-the-business-value-of-artificial-intelligence-w.

Menguc, B., Auh, S., Yeniaras, V., & Katsikeas, C. (2017). The role of climate: Implications for service employee engagement and customer service performance. Journal of the Academy of Marketing Science, 45(3), 428-451.

Metz, A. (2021). Pour one out for Pepper, the world’s first humanoid robot, now ‘discontinued.’ June 30. Accessed (Jan 7, 2022) at: https://www.techradar.com/nz/news/pour-one-out-for-pepper-the-worlds-first-humanoid-robot-now-discontinued.

Moreau, C. (2011). Inviting the amateurs into the studio: Understanding how consumer engagement in product design creates value. Journal of Product Innovation Management, 28(3), 409-410.

O’Riordan, L. & Fairbass, J. (2014). Managing CSR stakeholder engagement: A new conceptual framework. Journal of Business Ethics, 125, 121-145.

Perez-Vega, R., Kaartemo, V., Lages, C., Razavi, N., & Männisto, J. (2021). Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework. Journal of Business Research, 129(May), 902-910.

PwC (2019). Digital product development 2025: Agile, collaborative and customer centric. Accessed (Mar 26, 2022) at: https://www.pwc.de/de/digitale-transformation/pwc-studie-digital-product-development-2025.pdf

PwC (2021). Sizing the prize – PwC’s global artificial intelligence study: Exploiting the AI revolution. Accessed (Feb 17, 2022) at: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.

Raisch, S. & Krakowski, S. (2022). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, OnlineFirst, DOI: https://doi.org/10.5465/amr.2018.0072.

Sjödin, D., Frishammar, J., & Thorgen, S. (2019). How individuals engage in the absorption of new external knowledge: A process model of absorptive capacity. Journal of Product Innovation Management, 36(3), 356-380.

Spanjol, J. & Noble, C. (2020). From the editors: A catalyst and special issue on design thinking and innovation. Journal of Product Innovation Management, 37(3), 210-211.

Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227.

Viglia, G., Pera, R., & Bigné, E. (2018). The determinants of stakeholder engagement in digital platforms. Journal of Business Research, 89(Aug), 404-410.

Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2021). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. International Journal of Human Resource Management, OnlineFirst, DOI: https://doi.org/10.1080/09585192.2020.1871398.

Watson, R., Wilson, H., Smart, P., & MacDonald, E. (2018). Harnessing difference: A capability-based framework for stakeholder engagement in environmental innovation. Journal of Product Innovation Management, 35(2), 254-279.

Wetzels (2021). The road ahead is digital for innovation management and there is no way back. Journal of Product Innovation Management, 38(2), 245-247. Wijayati, D.T., Rahman, Z., Fahrullah, A., Rahman, M.F.W., Arifah, I.D.C., and Kautsar, A. (2022). A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership. International Journal of Manpower, In press, DOI: https://doi.org/10.1108/IJM-07-2021-0423.